生物信息学工具用于测序复杂度估计
Yuriy L Orlov1,2,3, Nina G Orlova4
1The Digital Health Institute, I.M. Sechenov First Moscow State Medical University of the Russian Ministry of Health (Sechenov University), Moscow, 119991 Russia.
Biophysical reviews
|November 17, 2023
概括
本综述探讨了文本复杂性估计方法和用于分析DNA和蛋白质序列的生物信息学工具. 这些方法对于理解基因组功能,基因调节和进化模式至关重要.
科学领域:
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
- 基因组学就是基因组学.
背景情况:
- 序列复杂性分析对于染色体功能和基因转录调节的生物物理模型至关重要.
- 了解DNA和蛋白质中低复杂度区域是基因组规模分析和进化研究的关键.
研究的目的:
- 审查当前的生物信息学工具和方法来估计序列复杂性.
- 讨论基因组分析,突变热点识别和蛋白质序列分析中的应用.
- 突出新型机器学习应用和大规模基因组复杂性比较.
主要方法:
- 使用信息和度来估计文本的复杂性.
- 采用压缩方法,主要是修改的Lempel-Ziv压缩.
- 在序列复杂性分析中应用组合和语言测量方法.
主要成果:
- 复杂性分析有助于基因组序列分割,重复识别和下一代测序阅读的分析.
- 这些方法适用于突变热点分析,短读质量控制和无对齐基因组比较.
- 序列复杂性分析扩展到蛋白质,揭示了具有重要的生物和结构功能的保存区域.
结论:
- 序列复杂性估计工具在网上广泛可用于大规模基因组分析.
- 机器学习越来越多地整合了序列压缩和复杂性算法用于基因组分类.
- 复杂性分析提供了对基因调节,蛋白质结构和病毒基因组进化 (包括冠状病毒) 的见解.
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